Adding Space to Disease Models: A Case Study with COVID-19 in Oregon, USA

Nathan H. Schumaker and Sydney M. Watkins
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Nathan H. Schumaker: Department of Fisheries and Wildlife, Oregon State University Corvallis, Corvallis, OR 97331, USA
Sydney M. Watkins: Computational Ecology Group, Canmore, AB T1W 3L4, Canada

Land, 2021, vol. 10, issue 4, 1-13

Abstract: We selected the COVID-19 outbreak in the state of Oregon, USA as a system for developing a general geographically nuanced epidemiological forecasting model that balances simplicity, realism, and accessibility. Using the life history simulator HexSim, we inserted a mathematical SIRD disease model into a spatially explicit framework, creating a distributed array of linked compartment models. Our spatial model introduced few additional parameters, but casting the SIRD equations into a geographic setting significantly altered the system’s emergent dynamics. Relative to the non-spatial model, our simple spatial model better replicated the record of observed infection rates in Oregon. We also observed that estimates of vaccination efficacy drawn from the non-spatial model tended to be higher than those obtained from models that incorporate geographic variation. Our spatially explicit SIRD simulations of COVID-19 in Oregon suggest that modest additions of spatial complexity can bring considerable realism to a traditional disease model.

Keywords: HexSim; spatially explicit model; simulation model; SIRD model; COVID-19; epidemiology; compartment model (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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